In the post-genomic era, the question of how genes give rise to the observable diversity of morphology, physiology, behaviour and disease susceptibility is becoming of one central importance. Even in a model system as well studied as the fruit fly Drosophila melanogaster, the function of the vast majority of genes, and the mechanisms by which they give rise to such diversity, remains unknown.
Drosophila behaviour represents a sensitive system in which to evaluate novel methods of determining gene function. Traditionally the analysis of behavioural phenotypes has represented a time consuming, highly subjective process. I have developed a suite of automated analysis tools (the BeFly! package) that has not only made such analyses both quicker and more objective, but has also allowed data to be examined in greater depth by making complex algorithms more accessible to users.
The BeFly! package was initially used to characterise a serendipitously identified circadian mutant strain provisionally named Party on. As the Party on gene could not be conclusively mapped, and a meta analysis of existing circadian microarray data suggested that many circadian genes remained to be identified, BeFly!’s high throughput tools were employed in a novel systems biology screen in which phenotypic analysis was combined with gene expression data to identify likely gene function.
This approach generated a number of novel candidate clock genes, the roles of which were further analysed using RNAi knockdown, confirming that the neuropeptide gene Adipokinetic hormone-like played a role in the clock mechanism. Given the success of our new strategy, it was widened to identify genes controlling sleep in Drosophila, leading to the identification of several genes associated with distinct aspects of sleep.
In conclusion, the tools and methods developed in this thesis represent a novel, sensitive method for determining gene function applicable beyond the Drosophila model.